Quantum-Si
Senior Scientist, Computational Biology
Quantum-Si, San Diego, California, United States, 92189
Overview
We are seeking a highly motivated and experienced Senior Scientist with expertise in computational biology and machine learning to join the Data Science & Algorithms Team. This role focuses on designing and optimizing protein binders with high affinity for N-terminal amino acid targets, a critical component of our Next-Generation Protein Sequencing kit. You will work at the intersection of machine learning, protein engineering, and structural biology, leveraging state-of-the-art algorithms and experimental feedback to develop novel protein scaffolds with tailored binding characteristics. The ideal candidate will have a deep background in Computational Biology, Bioinformatics, Data Science, or a related field with 5+ years of relevant academic or industry experience. The candidate also must have a strong knowledge of programming languages (e.g. Python, Bash) and experience with developing or fine-tuning machine learning models. Candidates with a demonstrated ability to apply machine learning to protein design, structure-function prediction, or generative modeling are especially encouraged to apply. Familiarity with state-of-the-art protein modeling software (e.g. AlphaFold, ProteinMPNN) is a plus. Responsibilities
Design, model, and computationally screen protein binders for selective binding to N-terminal amino acid motifs. Develop and optimize binder scaffolds using a combination of structure-based design, ML-driven design, and generative protein modeling tools. Collaborate with wet-lab teams to iteratively test, validate, and refine designs using experimental feedback. Innovate new computational pipelines for high-throughput protein binder discovery. Evaluate binding energetics, specificity, and structural feasibility using in silico approaches. Qualifications
Ph.D. in Computational Biology, Bioinformatics, Computer Science, Data Science, or a related computational/scientific field Skilled in ML model development and/or fine-tuning, especially for protein structure-function prediction and generative protein design Experience integrating experimental feedback loops into computational pipelines to improve design success Experience developing custom computational methods or ML approaches to guide protein design toward desired structural/functional properties Proficient in programming with Python (preferred) and/or other scripting languages such as Bash; familiarity with JupyterLab, Jupyter Notebooks, or similar environments for data analysis, interactive modeling, and prototyping Strong analytical thinking and practical problem-solving skills Excellent scientific communication and documentation skills, including data summarization and visualization using Python Additional desirable skills
Strong understanding of protein-protein and protein-peptide interactions; hands-on experience with in silico analyses Familiarity with protein structure prediction and design using AlphaFold, ProteinMPNN, RFDiffusion, ESM, Rosetta, etc. Experience designing binders against unstructured peptide regions, including terminal epitopes or motifs Familiarity with GPU-accelerated computing and scaling workflows using HPC or cloud resources Experience with Git Compensation
The estimated base salary range for this role based in the United States is $130,000 - $155,000. Compensation decisions depend on factors including level, skills, knowledge, location, internal equity, and market data. Full-time employees are eligible for discretionary bonus and equity. Equal Opportunity
Quantum-Si is an E-Verify and equal opportunity employer regardless of race, color, ancestry, religion, gender, national origin, sexual orientation, age, citizenship, marital status, disability or veteran status. All information will be kept confidential according to EEO guidelines.
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We are seeking a highly motivated and experienced Senior Scientist with expertise in computational biology and machine learning to join the Data Science & Algorithms Team. This role focuses on designing and optimizing protein binders with high affinity for N-terminal amino acid targets, a critical component of our Next-Generation Protein Sequencing kit. You will work at the intersection of machine learning, protein engineering, and structural biology, leveraging state-of-the-art algorithms and experimental feedback to develop novel protein scaffolds with tailored binding characteristics. The ideal candidate will have a deep background in Computational Biology, Bioinformatics, Data Science, or a related field with 5+ years of relevant academic or industry experience. The candidate also must have a strong knowledge of programming languages (e.g. Python, Bash) and experience with developing or fine-tuning machine learning models. Candidates with a demonstrated ability to apply machine learning to protein design, structure-function prediction, or generative modeling are especially encouraged to apply. Familiarity with state-of-the-art protein modeling software (e.g. AlphaFold, ProteinMPNN) is a plus. Responsibilities
Design, model, and computationally screen protein binders for selective binding to N-terminal amino acid motifs. Develop and optimize binder scaffolds using a combination of structure-based design, ML-driven design, and generative protein modeling tools. Collaborate with wet-lab teams to iteratively test, validate, and refine designs using experimental feedback. Innovate new computational pipelines for high-throughput protein binder discovery. Evaluate binding energetics, specificity, and structural feasibility using in silico approaches. Qualifications
Ph.D. in Computational Biology, Bioinformatics, Computer Science, Data Science, or a related computational/scientific field Skilled in ML model development and/or fine-tuning, especially for protein structure-function prediction and generative protein design Experience integrating experimental feedback loops into computational pipelines to improve design success Experience developing custom computational methods or ML approaches to guide protein design toward desired structural/functional properties Proficient in programming with Python (preferred) and/or other scripting languages such as Bash; familiarity with JupyterLab, Jupyter Notebooks, or similar environments for data analysis, interactive modeling, and prototyping Strong analytical thinking and practical problem-solving skills Excellent scientific communication and documentation skills, including data summarization and visualization using Python Additional desirable skills
Strong understanding of protein-protein and protein-peptide interactions; hands-on experience with in silico analyses Familiarity with protein structure prediction and design using AlphaFold, ProteinMPNN, RFDiffusion, ESM, Rosetta, etc. Experience designing binders against unstructured peptide regions, including terminal epitopes or motifs Familiarity with GPU-accelerated computing and scaling workflows using HPC or cloud resources Experience with Git Compensation
The estimated base salary range for this role based in the United States is $130,000 - $155,000. Compensation decisions depend on factors including level, skills, knowledge, location, internal equity, and market data. Full-time employees are eligible for discretionary bonus and equity. Equal Opportunity
Quantum-Si is an E-Verify and equal opportunity employer regardless of race, color, ancestry, religion, gender, national origin, sexual orientation, age, citizenship, marital status, disability or veteran status. All information will be kept confidential according to EEO guidelines.
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